Overview

Dataset statistics

Number of variables33
Number of observations7043
Missing cells5174
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 MiB
Average record size in memory1.5 KiB

Variable types

CAT17
BOOL8
NUM7
UNSUPPORTED1

Warnings

Count has constant value "7043" Constant
Country has constant value "7043" Constant
State has constant value "7043" Constant
City has a high cardinality: 1129 distinct values High cardinality
Lat Long has a high cardinality: 1652 distinct values High cardinality
Churn Reason has 5174 (73.5%) missing values Missing
Lat Long is uniformly distributed Uniform
CustomerID has unique values Unique
Total Charges is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2021-10-27 20:45:37.198837
Analysis finished2021-10-27 20:45:47.586957
Duration10.39 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

CustomerID
Categorical

UNIQUE

Distinct7043
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
5555-RNPGT
 
1
3181-VTHOE
 
1
1113-IUJYX
 
1
6253-WRFHY
 
1
6253-GNHWH
 
1
Other values (7038)
7038 
ValueCountFrequency (%) 
5555-RNPGT1< 0.1%
 
3181-VTHOE1< 0.1%
 
1113-IUJYX1< 0.1%
 
6253-WRFHY1< 0.1%
 
6253-GNHWH1< 0.1%
 
6599-SFQVE1< 0.1%
 
0859-YGKFW1< 0.1%
 
2558-BUOZZ1< 0.1%
 
1325-USMEC1< 0.1%
 
3146-JTQHR1< 0.1%
 
3129-AAQOU1< 0.1%
 
6258-PVZWJ1< 0.1%
 
7541-YLXCL1< 0.1%
 
5141-ZUVBH1< 0.1%
 
2898-LSJGD1< 0.1%
 
4878-BUNFV1< 0.1%
 
0936-NQLJU1< 0.1%
 
8263-OKETD1< 0.1%
 
4112-LUEIZ1< 0.1%
 
7267-FRMJW1< 0.1%
 
1834-WULEG1< 0.1%
 
5073-WXOYN1< 0.1%
 
1785-BPHTP1< 0.1%
 
5649-RXQTV1< 0.1%
 
9200-NLNPD1< 0.1%
 
Other values (7018)701899.6%
 
2021-10-27T13:45:47.692603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique7043 ?
Unique (%)100.0%
2021-10-27T13:45:47.811801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters37
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
-704310.0%
 
229014.1%
 
928814.1%
 
628704.1%
 
728364.0%
 
028314.0%
 
828124.0%
 
528104.0%
 
327914.0%
 
127263.9%
 
427143.9%
 
O14422.0%
 
H13962.0%
 
B13932.0%
 
S13862.0%
 
V13822.0%
 
T13742.0%
 
C13681.9%
 
Z13681.9%
 
K13631.9%
 
F13631.9%
 
L13631.9%
 
E13611.9%
 
N13611.9%
 
D13571.9%
 
Other values (12)1593822.6%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter3521550.0%
 
Decimal Number2817240.0%
 
Dash Punctuation704310.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
2290110.3%
 
9288110.2%
 
6287010.2%
 
7283610.1%
 
0283110.0%
 
8281210.0%
 
5281010.0%
 
327919.9%
 
127269.7%
 
427149.6%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-7043100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O14424.1%
 
H13964.0%
 
B13934.0%
 
S13863.9%
 
V13823.9%
 
T13743.9%
 
C13683.9%
 
Z13683.9%
 
K13633.9%
 
F13633.9%
 
L13633.9%
 
E13613.9%
 
N13613.9%
 
D13573.9%
 
R13553.8%
 
Q13533.8%
 
M13523.8%
 
Y13503.8%
 
G13503.8%
 
A13463.8%
 
W13373.8%
 
J13293.8%
 
P13233.8%
 
U12993.7%
 
I12833.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3521550.0%
 
Latin3521550.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
-704320.0%
 
229018.2%
 
928818.2%
 
628708.1%
 
728368.1%
 
028318.0%
 
828128.0%
 
528108.0%
 
327917.9%
 
127267.7%
 
427147.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
O14424.1%
 
H13964.0%
 
B13934.0%
 
S13863.9%
 
V13823.9%
 
T13743.9%
 
C13683.9%
 
Z13683.9%
 
K13633.9%
 
F13633.9%
 
L13633.9%
 
E13613.9%
 
N13613.9%
 
D13573.9%
 
R13553.8%
 
Q13533.8%
 
M13523.8%
 
Y13503.8%
 
G13503.8%
 
A13463.8%
 
W13373.8%
 
J13293.8%
 
P13233.8%
 
U12993.7%
 
I12833.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII70430100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
-704310.0%
 
229014.1%
 
928814.1%
 
628704.1%
 
728364.0%
 
028314.0%
 
828124.0%
 
528104.0%
 
327914.0%
 
127263.9%
 
427143.9%
 
O14422.0%
 
H13962.0%
 
B13932.0%
 
S13862.0%
 
V13822.0%
 
T13742.0%
 
C13681.9%
 
Z13681.9%
 
K13631.9%
 
F13631.9%
 
L13631.9%
 
E13611.9%
 
N13611.9%
 
D13571.9%
 
Other values (12)1593822.6%
 

Count
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
1
7043 
ValueCountFrequency (%) 
17043100.0%
 
2021-10-27T13:45:47.872640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Country
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
United States
7043 
ValueCountFrequency (%) 
United States7043100.0%
 
2021-10-27T13:45:47.934712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:47.998585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:48.062261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length13
Min length13

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t2112923.1%
 
e1408615.4%
 
U70437.7%
 
n70437.7%
 
i70437.7%
 
d70437.7%
 
70437.7%
 
S70437.7%
 
a70437.7%
 
s70437.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7043076.9%
 
Uppercase Letter1408615.4%
 
Space Separator70437.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
U704350.0%
 
S704350.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t2112930.0%
 
e1408620.0%
 
n704310.0%
 
i704310.0%
 
d704310.0%
 
a704310.0%
 
s704310.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
7043100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin8451692.3%
 
Common70437.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t2112925.0%
 
e1408616.7%
 
U70438.3%
 
n70438.3%
 
i70438.3%
 
d70438.3%
 
S70438.3%
 
a70438.3%
 
s70438.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
7043100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII91559100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t2112923.1%
 
e1408615.4%
 
U70437.7%
 
n70437.7%
 
i70437.7%
 
d70437.7%
 
70437.7%
 
S70437.7%
 
a70437.7%
 
s70437.7%
 

State
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
California
7043 
ValueCountFrequency (%) 
California7043100.0%
 
2021-10-27T13:45:48.157005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:48.213854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:48.279706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a1408620.0%
 
i1408620.0%
 
C704310.0%
 
l704310.0%
 
f704310.0%
 
o704310.0%
 
r704310.0%
 
n704310.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter6338790.0%
 
Uppercase Letter704310.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C7043100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1408622.2%
 
i1408622.2%
 
l704311.1%
 
f704311.1%
 
o704311.1%
 
r704311.1%
 
n704311.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin70430100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a1408620.0%
 
i1408620.0%
 
C704310.0%
 
l704310.0%
 
f704310.0%
 
o704310.0%
 
r704310.0%
 
n704310.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII70430100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a1408620.0%
 
i1408620.0%
 
C704310.0%
 
l704310.0%
 
f704310.0%
 
o704310.0%
 
r704310.0%
 
n704310.0%
 

City
Categorical

HIGH CARDINALITY

Distinct1129
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Los Angeles
 
305
San Diego
 
150
San Jose
 
112
Sacramento
 
108
San Francisco
 
104
Other values (1124)
6264 
ValueCountFrequency (%) 
Los Angeles3054.3%
 
San Diego1502.1%
 
San Jose1121.6%
 
Sacramento1081.5%
 
San Francisco1041.5%
 
Fresno640.9%
 
Long Beach600.9%
 
Oakland520.7%
 
Stockton440.6%
 
Glendale400.6%
 
Bakersfield400.6%
 
Berkeley320.5%
 
Riverside320.5%
 
Whittier300.4%
 
Pasadena300.4%
 
Anaheim280.4%
 
Santa Barbara280.4%
 
Irvine280.4%
 
Modesto280.4%
 
San Bernardino280.4%
 
Burbank250.4%
 
Chula Vista250.4%
 
Torrance250.4%
 
Santa Monica250.4%
 
Inglewood250.4%
 
Other values (1104)557579.2%
 
2021-10-27T13:45:48.402983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:48.532622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length9
Mean length9.223200341
Min length3

Overview of Unicode Properties

Unique unicode characters52
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a695110.7%
 
e61529.5%
 
n51177.9%
 
o49437.6%
 
l40076.2%
 
r36495.6%
 
i33715.2%
 
32765.0%
 
s29244.5%
 
t27084.2%
 
d16492.5%
 
S14652.3%
 
c14202.2%
 
g12391.9%
 
u11131.7%
 
C10101.6%
 
h10051.5%
 
m9751.5%
 
y9491.5%
 
L9011.4%
 
B7521.2%
 
k7501.2%
 
A6731.0%
 
v6591.0%
 
M6191.0%
 
Other values (27)668210.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5136479.1%
 
Uppercase Letter1031915.9%
 
Space Separator32765.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S146514.2%
 
C10109.8%
 
L9018.7%
 
B7527.3%
 
A6736.5%
 
M6196.0%
 
P6135.9%
 
R4564.4%
 
F4424.3%
 
V4204.1%
 
D4033.9%
 
H4013.9%
 
G3763.6%
 
W3032.9%
 
O2932.8%
 
T2702.6%
 
N2292.2%
 
J1971.9%
 
E1961.9%
 
I1221.2%
 
K800.8%
 
Y590.6%
 
U220.2%
 
Q130.1%
 
Z4< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a695113.5%
 
e615212.0%
 
n511710.0%
 
o49439.6%
 
l40077.8%
 
r36497.1%
 
i33716.6%
 
s29245.7%
 
t27085.3%
 
d16493.2%
 
c14202.8%
 
g12392.4%
 
u11132.2%
 
h10052.0%
 
m9751.9%
 
y9491.8%
 
k7501.5%
 
v6591.3%
 
p5081.0%
 
w4690.9%
 
b3920.8%
 
f1970.4%
 
z880.2%
 
j560.1%
 
x490.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3276100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin6168395.0%
 
Common32765.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a695111.3%
 
e615210.0%
 
n51178.3%
 
o49438.0%
 
l40076.5%
 
r36495.9%
 
i33715.5%
 
s29244.7%
 
t27084.4%
 
d16492.7%
 
S14652.4%
 
c14202.3%
 
g12392.0%
 
u11131.8%
 
C10101.6%
 
h10051.6%
 
m9751.6%
 
y9491.5%
 
L9011.5%
 
B7521.2%
 
k7501.2%
 
A6731.1%
 
v6591.1%
 
M6191.0%
 
P6131.0%
 
Other values (26)60699.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
3276100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII64959100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a695110.7%
 
e61529.5%
 
n51177.9%
 
o49437.6%
 
l40076.2%
 
r36495.6%
 
i33715.2%
 
32765.0%
 
s29244.5%
 
t27084.2%
 
d16492.5%
 
S14652.3%
 
c14202.2%
 
g12391.9%
 
u11131.7%
 
C10101.6%
 
h10051.5%
 
m9751.5%
 
y9491.5%
 
L9011.4%
 
B7521.2%
 
k7501.2%
 
A6731.0%
 
v6591.0%
 
M6191.0%
 
Other values (27)668210.3%
 

Zip Code
Real number (ℝ≥0)

Distinct1652
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93521.96465
Minimum90001
Maximum96161
Zeros0
Zeros (%)0.0%
Memory size55.1 KiB
2021-10-27T13:45:48.666301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum90001
5-th percentile90232
Q192102
median93552
Q395351
95-th percentile96031
Maximum96161
Range6160
Interquartile range (IQR)3249

Descriptive statistics

Standard deviation1865.794555
Coefficient of variation (CV)0.01995033533
Kurtosis-1.154042612
Mean93521.96465
Median Absolute Deviation (MAD)1641
Skewness-0.251463488
Sum658675197
Variance3481189.323
MonotocityNot monotonic
2021-10-27T13:45:48.787840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9213950.1%
 
9130650.1%
 
9060250.1%
 
9050250.1%
 
9040250.1%
 
9030250.1%
 
9029050.1%
 
9027850.1%
 
9027450.1%
 
9027050.1%
 
9026650.1%
 
9026250.1%
 
9230750.1%
 
9025450.1%
 
9025050.1%
 
9024250.1%
 
9228350.1%
 
9023050.1%
 
9227550.1%
 
9022250.1%
 
9226750.1%
 
9060650.1%
 
9063050.1%
 
9063850.1%
 
9082250.1%
 
Other values (1627)691898.2%
 
ValueCountFrequency (%) 
9000150.1%
 
9000250.1%
 
9000350.1%
 
9000450.1%
 
9000550.1%
 
9000650.1%
 
9000750.1%
 
9000850.1%
 
9001050.1%
 
9001150.1%
 
ValueCountFrequency (%) 
9616140.1%
 
9615040.1%
 
9614840.1%
 
9614640.1%
 
9614540.1%
 
9614340.1%
 
9614240.1%
 
9614140.1%
 
9614040.1%
 
9613740.1%
 

Lat Long
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1652
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
34.1678, -116.86433
 
5
33.771612, -118.143866
 
5
33.86532, -118.396336
 
5
34.13658, -118.245839
 
5
34.027337, -118.28515
 
5
Other values (1647)
7018 
ValueCountFrequency (%) 
34.1678, -116.8643350.1%
 
33.771612, -118.14386650.1%
 
33.86532, -118.39633650.1%
 
34.13658, -118.24583950.1%
 
34.027337, -118.2851550.1%
 
34.127621, -117.71786350.1%
 
32.578103, -117.01297550.1%
 
34.052917, -118.25517850.1%
 
34.370378, -118.50411850.1%
 
34.281911, -118.55621850.1%
 
33.362575, -117.29964450.1%
 
34.202494, -118.44804850.1%
 
34.146635, -118.13922550.1%
 
34.199787, -118.6849350.1%
 
34.147149, -118.46336550.1%
 
33.998471, -117.97375850.1%
 
33.840524, -118.14840350.1%
 
33.850504, -118.03989250.1%
 
34.001617, -118.22227450.1%
 
32.770393, -115.6091550.1%
 
34.076259, -118.31071550.1%
 
32.811001, -115.15286550.1%
 
32.579134, -117.11900950.1%
 
34.099869, -118.32684350.1%
 
34.178483, -118.43179150.1%
 
Other values (1627)691898.2%
 
2021-10-27T13:45:48.940466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:49.156902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length22
Mean length21.77708363
Min length18

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories4 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
12039713.3%
 
31644610.7%
 
.140869.2%
 
2137068.9%
 
8110027.2%
 
7107827.0%
 
4105126.9%
 
995456.2%
 
689505.8%
 
586755.7%
 
081465.3%
 
,70434.6%
 
70434.6%
 
-70434.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number11816177.0%
 
Other Punctuation2112913.8%
 
Space Separator70434.6%
 
Dash Punctuation70434.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
12039717.3%
 
31644613.9%
 
21370611.6%
 
8110029.3%
 
7107829.1%
 
4105128.9%
 
995458.1%
 
689507.6%
 
586757.3%
 
081466.9%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1408666.7%
 
,704333.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
7043100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-7043100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common153376100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
12039713.3%
 
31644610.7%
 
.140869.2%
 
2137068.9%
 
8110027.2%
 
7107827.0%
 
4105126.9%
 
995456.2%
 
689505.8%
 
586755.7%
 
081465.3%
 
,70434.6%
 
70434.6%
 
-70434.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII153376100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
12039713.3%
 
31644610.7%
 
.140869.2%
 
2137068.9%
 
8110027.2%
 
7107827.0%
 
4105126.9%
 
995456.2%
 
689505.8%
 
586755.7%
 
081465.3%
 
,70434.6%
 
70434.6%
 
-70434.6%
 

Latitude
Real number (ℝ≥0)

Distinct1652
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.28244138
Minimum32.555828
Maximum41.962127
Zeros0
Zeros (%)0.0%
Memory size55.1 KiB
2021-10-27T13:45:49.262549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum32.555828
5-th percentile32.980678
Q134.030915
median36.391777
Q338.224869
95-th percentile40.557314
Maximum41.962127
Range9.406299
Interquartile range (IQR)4.193954

Descriptive statistics

Standard deviation2.45572259
Coefficient of variation (CV)0.06768349913
Kurtosis-1.135607142
Mean36.28244138
Median Absolute Deviation (MAD)2.263493
Skewness0.3038672929
Sum255537.2346
Variance6.030573437
MonotocityNot monotonic
2021-10-27T13:45:49.379948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
34.23131850.1%
 
34.19538650.1%
 
32.78516550.1%
 
34.1464950.1%
 
32.9935650.1%
 
33.07983450.1%
 
33.11902850.1%
 
34.10211950.1%
 
33.77161250.1%
 
34.45700550.1%
 
34.09786350.1%
 
34.13741250.1%
 
33.04454150.1%
 
32.76250650.1%
 
34.211250.1%
 
34.04314450.1%
 
34.17207150.1%
 
34.06636750.1%
 
33.8885650.1%
 
32.88692550.1%
 
32.69709850.1%
 
34.01835450.1%
 
33.21525150.1%
 
34.16723650.1%
 
33.93682750.1%
 
Other values (1627)691898.2%
 
ValueCountFrequency (%) 
32.55582850.1%
 
32.57810350.1%
 
32.57913450.1%
 
32.58755750.1%
 
32.60501250.1%
 
32.60796450.1%
 
32.61946550.1%
 
32.62299950.1%
 
32.63679250.1%
 
32.6416450.1%
 
ValueCountFrequency (%) 
41.96212740.1%
 
41.95068340.1%
 
41.94921640.1%
 
41.93220740.1%
 
41.92417440.1%
 
41.86790840.1%
 
41.83190140.1%
 
41.81659540.1%
 
41.81352140.1%
 
41.76970940.1%
 

Longitude
Real number (ℝ)

Distinct1651
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.7988801
Minimum-124.301372
Maximum-114.192901
Zeros0
Zeros (%)0.0%
Memory size55.1 KiB
2021-10-27T13:45:49.498631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-124.301372
5-th percentile-122.998726
Q1-121.815412
median-119.730885
Q3-118.043237
95-th percentile-116.76058
Maximum-114.192901
Range10.108471
Interquartile range (IQR)3.772175

Descriptive statistics

Standard deviation2.157889095
Coefficient of variation (CV)-0.01801259823
Kurtosis-1.136049757
Mean-119.7988801
Median Absolute Deviation (MAD)1.824786
Skewness-0.04079238284
Sum-843743.5124
Variance4.656485347
MonotocityNot monotonic
2021-10-27T13:45:49.623680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-121.99481380.1%
 
-118.37044250.1%
 
-117.80787450.1%
 
-118.148650.1%
 
-117.07261950.1%
 
-118.61558350.1%
 
-116.7605850.1%
 
-118.37249850.1%
 
-118.45947250.1%
 
-118.38106150.1%
 
-116.59345650.1%
 
-117.29964450.1%
 
-116.29108950.1%
 
-116.56191750.1%
 
-117.88684450.1%
 
-116.8643350.1%
 
-117.14245450.1%
 
-118.06261150.1%
 
-118.48605650.1%
 
-118.07407150.1%
 
-114.3651450.1%
 
-118.14595950.1%
 
-116.98607950.1%
 
-118.0826350.1%
 
-115.76977350.1%
 
Other values (1626)691598.2%
 
ValueCountFrequency (%) 
-124.30137240.1%
 
-124.24005140.1%
 
-124.21737840.1%
 
-124.21090240.1%
 
-124.18997740.1%
 
-124.16323440.1%
 
-124.1542840.1%
 
-124.12150440.1%
 
-124.10889740.1%
 
-124.09873940.1%
 
ValueCountFrequency (%) 
-114.19290150.1%
 
-114.3651450.1%
 
-114.70225640.1%
 
-114.7161250.1%
 
-114.71796450.1%
 
-114.75833450.1%
 
-114.85078450.1%
 
-115.15286550.1%
 
-115.19185750.1%
 
-115.25700950.1%
 

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Male
3555 
Female
3488 
ValueCountFrequency (%) 
Male355550.5%
 
Female348849.5%
 
2021-10-27T13:45:49.730398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:49.789340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:49.868080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length4
Mean length4.990487008
Min length4

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e1053130.0%
 
a704320.0%
 
l704320.0%
 
M355510.1%
 
F34889.9%
 
m34889.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2810580.0%
 
Uppercase Letter704320.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M355550.5%
 
F348849.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e1053137.5%
 
a704325.1%
 
l704325.1%
 
m348812.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin35148100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e1053130.0%
 
a704320.0%
 
l704320.0%
 
M355510.1%
 
F34889.9%
 
m34889.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII35148100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e1053130.0%
 
a704320.0%
 
l704320.0%
 
M355510.1%
 
F34889.9%
 
m34889.9%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
5901 
Yes
1142 
ValueCountFrequency (%) 
No590183.8%
 
Yes114216.2%
 
2021-10-27T13:45:49.940886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Partner
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
3641 
Yes
3402 
ValueCountFrequency (%) 
No364151.7%
 
Yes340248.3%
 
2021-10-27T13:45:49.980778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Dependents
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
5416 
Yes
1627 
ValueCountFrequency (%) 
No541676.9%
 
Yes162723.1%
 
2021-10-27T13:45:50.016683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Tenure Months
Real number (ℝ≥0)

Distinct73
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.37114866
Minimum0
Maximum72
Zeros11
Zeros (%)0.2%
Memory size55.1 KiB
2021-10-27T13:45:50.100120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median29
Q355
95-th percentile72
Maximum72
Range72
Interquartile range (IQR)46

Descriptive statistics

Standard deviation24.55948102
Coefficient of variation (CV)0.7586842618
Kurtosis-1.387371636
Mean32.37114866
Median Absolute Deviation (MAD)22
Skewness0.2395397496
Sum227990
Variance603.1681081
MonotocityNot monotonic
2021-10-27T13:45:50.228015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
16138.7%
 
723625.1%
 
22383.4%
 
32002.8%
 
41762.5%
 
711702.4%
 
51331.9%
 
71311.9%
 
81231.7%
 
701191.7%
 
91191.7%
 
121171.7%
 
101161.6%
 
61101.6%
 
131091.5%
 
681001.4%
 
15991.4%
 
11991.4%
 
67981.4%
 
18971.4%
 
69951.3%
 
24941.3%
 
22901.3%
 
66891.3%
 
35881.2%
 
Other values (48)325846.3%
 
ValueCountFrequency (%) 
0110.2%
 
16138.7%
 
22383.4%
 
32002.8%
 
41762.5%
 
51331.9%
 
61101.6%
 
71311.9%
 
81231.7%
 
91191.7%
 
ValueCountFrequency (%) 
723625.1%
 
711702.4%
 
701191.7%
 
69951.3%
 
681001.4%
 
67981.4%
 
66891.3%
 
65761.1%
 
64801.1%
 
63721.0%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Yes
6361 
No
682 
ValueCountFrequency (%) 
Yes636190.3%
 
No6829.7%
 
2021-10-27T13:45:50.308800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Multiple Lines
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
3390 
Yes
2971 
No phone service
682 
ValueCountFrequency (%) 
No339048.1%
 
Yes297142.2%
 
No phone service6829.7%
 
2021-10-27T13:45:50.373627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:50.436459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:50.508689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length3
Mean length3.777509584
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e501718.9%
 
o475417.9%
 
N407215.3%
 
s365313.7%
 
Y297111.2%
 
13645.1%
 
p6822.6%
 
h6822.6%
 
n6822.6%
 
r6822.6%
 
v6822.6%
 
i6822.6%
 
c6822.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1819868.4%
 
Uppercase Letter704326.5%
 
Space Separator13645.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N407257.8%
 
Y297142.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e501727.6%
 
o475426.1%
 
s365320.1%
 
p6823.7%
 
h6823.7%
 
n6823.7%
 
r6823.7%
 
v6823.7%
 
i6823.7%
 
c6823.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1364100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2524194.9%
 
Common13645.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e501719.9%
 
o475418.8%
 
N407216.1%
 
s365314.5%
 
Y297111.8%
 
p6822.7%
 
h6822.7%
 
n6822.7%
 
r6822.7%
 
v6822.7%
 
i6822.7%
 
c6822.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
1364100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26605100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e501718.9%
 
o475417.9%
 
N407215.3%
 
s365313.7%
 
Y297111.2%
 
13645.1%
 
p6822.6%
 
h6822.6%
 
n6822.6%
 
r6822.6%
 
v6822.6%
 
i6822.6%
 
c6822.6%
 

Internet Service
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Fiber optic
3096 
DSL
2421 
No
1526 
ValueCountFrequency (%) 
Fiber optic309644.0%
 
DSL242134.4%
 
No152621.7%
 
2021-10-27T13:45:50.605166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:50.667998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:50.738760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length3
Mean length6.300014198
Min length2

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i619214.0%
 
o462210.4%
 
F30967.0%
 
b30967.0%
 
e30967.0%
 
r30967.0%
 
30967.0%
 
p30967.0%
 
t30967.0%
 
c30967.0%
 
D24215.5%
 
S24215.5%
 
L24215.5%
 
N15263.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2939066.2%
 
Uppercase Letter1188526.8%
 
Space Separator30967.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F309626.0%
 
D242120.4%
 
S242120.4%
 
L242120.4%
 
N152612.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i619221.1%
 
o462215.7%
 
b309610.5%
 
e309610.5%
 
r309610.5%
 
p309610.5%
 
t309610.5%
 
c309610.5%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3096100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4127593.0%
 
Common30967.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i619215.0%
 
o462211.2%
 
F30967.5%
 
b30967.5%
 
e30967.5%
 
r30967.5%
 
p30967.5%
 
t30967.5%
 
c30967.5%
 
D24215.9%
 
S24215.9%
 
L24215.9%
 
N15263.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
3096100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII44371100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i619214.0%
 
o462210.4%
 
F30967.0%
 
b30967.0%
 
e30967.0%
 
r30967.0%
 
30967.0%
 
p30967.0%
 
t30967.0%
 
c30967.0%
 
D24215.5%
 
S24215.5%
 
L24215.5%
 
N15263.4%
 

Online Security
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
3498 
Yes
2019 
No internet service
1526 
ValueCountFrequency (%) 
No349849.7%
 
Yes201928.7%
 
No internet service152621.7%
 
2021-10-27T13:45:50.839987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:50.912750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:50.988985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length3
Mean length5.970041176
Min length2

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e812319.3%
 
N502411.9%
 
o502411.9%
 
s35458.4%
 
30527.3%
 
i30527.3%
 
n30527.3%
 
t30527.3%
 
r30527.3%
 
Y20194.8%
 
v15263.6%
 
c15263.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3195276.0%
 
Uppercase Letter704316.8%
 
Space Separator30527.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N502471.3%
 
Y201928.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e812325.4%
 
o502415.7%
 
s354511.1%
 
i30529.6%
 
n30529.6%
 
t30529.6%
 
r30529.6%
 
v15264.8%
 
c15264.8%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3052100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3899592.7%
 
Common30527.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e812320.8%
 
N502412.9%
 
o502412.9%
 
s35459.1%
 
i30527.8%
 
n30527.8%
 
t30527.8%
 
r30527.8%
 
Y20195.2%
 
v15263.9%
 
c15263.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
3052100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII42047100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e812319.3%
 
N502411.9%
 
o502411.9%
 
s35458.4%
 
30527.3%
 
i30527.3%
 
n30527.3%
 
t30527.3%
 
r30527.3%
 
Y20194.8%
 
v15263.6%
 
c15263.6%
 

Online Backup
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
3088 
Yes
2429 
No internet service
1526 
ValueCountFrequency (%) 
No308843.8%
 
Yes242934.5%
 
No internet service152621.7%
 
2021-10-27T13:45:51.087771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:51.152780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:51.225519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length3
Mean length6.028255005
Min length2

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e853320.1%
 
N461410.9%
 
o461410.9%
 
s39559.3%
 
30527.2%
 
i30527.2%
 
n30527.2%
 
t30527.2%
 
r30527.2%
 
Y24295.7%
 
v15263.6%
 
c15263.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3236276.2%
 
Uppercase Letter704316.6%
 
Space Separator30527.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N461465.5%
 
Y242934.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e853326.4%
 
o461414.3%
 
s395512.2%
 
i30529.4%
 
n30529.4%
 
t30529.4%
 
r30529.4%
 
v15264.7%
 
c15264.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3052100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3940592.8%
 
Common30527.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e853321.7%
 
N461411.7%
 
o461411.7%
 
s395510.0%
 
i30527.7%
 
n30527.7%
 
t30527.7%
 
r30527.7%
 
Y24296.2%
 
v15263.9%
 
c15263.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
3052100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII42457100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e853320.1%
 
N461410.9%
 
o461410.9%
 
s39559.3%
 
30527.2%
 
i30527.2%
 
n30527.2%
 
t30527.2%
 
r30527.2%
 
Y24295.7%
 
v15263.6%
 
c15263.6%
 
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
3095 
Yes
2422 
No internet service
1526 
ValueCountFrequency (%) 
No309543.9%
 
Yes242234.4%
 
No internet service152621.7%
 
2021-10-27T13:45:51.318898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:51.380734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:51.454942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length3
Mean length6.02726111
Min length2

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e852620.1%
 
N462110.9%
 
o462110.9%
 
s39489.3%
 
30527.2%
 
i30527.2%
 
n30527.2%
 
t30527.2%
 
r30527.2%
 
Y24225.7%
 
v15263.6%
 
c15263.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3235576.2%
 
Uppercase Letter704316.6%
 
Space Separator30527.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N462165.6%
 
Y242234.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e852626.4%
 
o462114.3%
 
s394812.2%
 
i30529.4%
 
n30529.4%
 
t30529.4%
 
r30529.4%
 
v15264.7%
 
c15264.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3052100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3939892.8%
 
Common30527.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e852621.6%
 
N462111.7%
 
o462111.7%
 
s394810.0%
 
i30527.7%
 
n30527.7%
 
t30527.7%
 
r30527.7%
 
Y24226.1%
 
v15263.9%
 
c15263.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
3052100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII42450100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e852620.1%
 
N462110.9%
 
o462110.9%
 
s39489.3%
 
30527.2%
 
i30527.2%
 
n30527.2%
 
t30527.2%
 
r30527.2%
 
Y24225.7%
 
v15263.6%
 
c15263.6%
 

Tech Support
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
3473 
Yes
2044 
No internet service
1526 
ValueCountFrequency (%) 
No347349.3%
 
Yes204429.0%
 
No internet service152621.7%
 
2021-10-27T13:45:51.564493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:51.635301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:51.708297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length3
Mean length5.973590799
Min length2

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e814819.4%
 
N499911.9%
 
o499911.9%
 
s35708.5%
 
30527.3%
 
i30527.3%
 
n30527.3%
 
t30527.3%
 
r30527.3%
 
Y20444.9%
 
v15263.6%
 
c15263.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3197776.0%
 
Uppercase Letter704316.7%
 
Space Separator30527.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N499971.0%
 
Y204429.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e814825.5%
 
o499915.6%
 
s357011.2%
 
i30529.5%
 
n30529.5%
 
t30529.5%
 
r30529.5%
 
v15264.8%
 
c15264.8%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3052100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3902092.7%
 
Common30527.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e814820.9%
 
N499912.8%
 
o499912.8%
 
s35709.1%
 
i30527.8%
 
n30527.8%
 
t30527.8%
 
r30527.8%
 
Y20445.2%
 
v15263.9%
 
c15263.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
3052100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII42072100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e814819.4%
 
N499911.9%
 
o499911.9%
 
s35708.5%
 
30527.3%
 
i30527.3%
 
n30527.3%
 
t30527.3%
 
r30527.3%
 
Y20444.9%
 
v15263.6%
 
c15263.6%
 

Streaming TV
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
2810 
Yes
2707 
No internet service
1526 
ValueCountFrequency (%) 
No281039.9%
 
Yes270738.4%
 
No internet service152621.7%
 
2021-10-27T13:45:51.810263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:51.876130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:51.948934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length3
Mean length6.067726821
Min length2

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e881120.6%
 
N433610.1%
 
o433610.1%
 
s42339.9%
 
30527.1%
 
i30527.1%
 
n30527.1%
 
t30527.1%
 
r30527.1%
 
Y27076.3%
 
v15263.6%
 
c15263.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3264076.4%
 
Uppercase Letter704316.5%
 
Space Separator30527.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N433661.6%
 
Y270738.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e881127.0%
 
o433613.3%
 
s423313.0%
 
i30529.4%
 
n30529.4%
 
t30529.4%
 
r30529.4%
 
v15264.7%
 
c15264.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3052100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3968392.9%
 
Common30527.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e881122.2%
 
N433610.9%
 
o433610.9%
 
s423310.7%
 
i30527.7%
 
n30527.7%
 
t30527.7%
 
r30527.7%
 
Y27076.8%
 
v15263.8%
 
c15263.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
3052100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII42735100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e881120.6%
 
N433610.1%
 
o433610.1%
 
s42339.9%
 
30527.1%
 
i30527.1%
 
n30527.1%
 
t30527.1%
 
r30527.1%
 
Y27076.3%
 
v15263.6%
 
c15263.6%
 

Streaming Movies
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
2785 
Yes
2732 
No internet service
1526 
ValueCountFrequency (%) 
No278539.5%
 
Yes273238.8%
 
No internet service152621.7%
 
2021-10-27T13:45:52.177956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:52.247770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:52.321181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length3
Mean length6.071276445
Min length2

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e883620.7%
 
N431110.1%
 
o431110.1%
 
s425810.0%
 
30527.1%
 
i30527.1%
 
n30527.1%
 
t30527.1%
 
r30527.1%
 
Y27326.4%
 
v15263.6%
 
c15263.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3266576.4%
 
Uppercase Letter704316.5%
 
Space Separator30527.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N431161.2%
 
Y273238.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e883627.1%
 
o431113.2%
 
s425813.0%
 
i30529.3%
 
n30529.3%
 
t30529.3%
 
r30529.3%
 
v15264.7%
 
c15264.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3052100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3970892.9%
 
Common30527.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e883622.3%
 
N431110.9%
 
o431110.9%
 
s425810.7%
 
i30527.7%
 
n30527.7%
 
t30527.7%
 
r30527.7%
 
Y27326.9%
 
v15263.8%
 
c15263.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
3052100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII42760100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e883620.7%
 
N431110.1%
 
o431110.1%
 
s425810.0%
 
30527.1%
 
i30527.1%
 
n30527.1%
 
t30527.1%
 
r30527.1%
 
Y27326.4%
 
v15263.6%
 
c15263.6%
 

Contract
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Month-to-month
3875 
Two year
1695 
One year
1473 
ValueCountFrequency (%) 
Month-to-month387555.0%
 
Two year169524.1%
 
One year147320.9%
 
2021-10-27T13:45:52.416928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:52.479801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:52.557083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length14
Mean length11.30115008
Min length8

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o1332016.7%
 
t1162514.6%
 
n922311.6%
 
h77509.7%
 
-77509.7%
 
e46415.8%
 
M38754.9%
 
m38754.9%
 
31684.0%
 
y31684.0%
 
a31684.0%
 
r31684.0%
 
T16952.1%
 
w16952.1%
 
O14731.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter6163377.4%
 
Dash Punctuation77509.7%
 
Uppercase Letter70438.8%
 
Space Separator31684.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M387555.0%
 
T169524.1%
 
O147320.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o1332021.6%
 
t1162518.9%
 
n922315.0%
 
h775012.6%
 
e46417.5%
 
m38756.3%
 
y31685.1%
 
a31685.1%
 
r31685.1%
 
w16952.8%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-7750100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3168100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin6867686.3%
 
Common1091813.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o1332019.4%
 
t1162516.9%
 
n922313.4%
 
h775011.3%
 
e46416.8%
 
M38755.6%
 
m38755.6%
 
y31684.6%
 
a31684.6%
 
r31684.6%
 
T16952.5%
 
w16952.5%
 
O14732.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
-775071.0%
 
316829.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII79594100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o1332016.7%
 
t1162514.6%
 
n922311.6%
 
h77509.7%
 
-77509.7%
 
e46415.8%
 
M38754.9%
 
m38754.9%
 
31684.0%
 
y31684.0%
 
a31684.0%
 
r31684.0%
 
T16952.1%
 
w16952.1%
 
O14731.9%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Yes
4171 
No
2872 
ValueCountFrequency (%) 
Yes417159.2%
 
No287240.8%
 
2021-10-27T13:45:52.620912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Payment Method
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Electronic check
2365 
Mailed check
1612 
Bank transfer (automatic)
1544 
Credit card (automatic)
1522 
ValueCountFrequency (%) 
Electronic check236533.6%
 
Mailed check161222.9%
 
Bank transfer (automatic)154421.9%
 
Credit card (automatic)152221.6%
 
2021-10-27T13:45:52.683600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:52.746433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:52.832897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length16
Mean length18.57021156
Min length12

Overview of Unicode Properties

Unique unicode characters23
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
c1727213.2%
 
a123549.4%
 
t115638.8%
 
e110208.4%
 
101097.7%
 
i85656.5%
 
r84976.5%
 
k55214.2%
 
n54534.2%
 
o54314.2%
 
d46563.6%
 
l39773.0%
 
h39773.0%
 
(30662.3%
 
u30662.3%
 
m30662.3%
 
)30662.3%
 
E23651.8%
 
M16121.2%
 
B15441.2%
 
s15441.2%
 
f15441.2%
 
C15221.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10750682.2%
 
Space Separator101097.7%
 
Uppercase Letter70435.4%
 
Open Punctuation30662.3%
 
Close Punctuation30662.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E236533.6%
 
M161222.9%
 
B154421.9%
 
C152221.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
c1727216.1%
 
a1235411.5%
 
t1156310.8%
 
e1102010.3%
 
i85658.0%
 
r84977.9%
 
k55215.1%
 
n54535.1%
 
o54315.1%
 
d46564.3%
 
l39773.7%
 
h39773.7%
 
u30662.9%
 
m30662.9%
 
s15441.4%
 
f15441.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
10109100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(3066100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)3066100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin11454987.6%
 
Common1624112.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
c1727215.1%
 
a1235410.8%
 
t1156310.1%
 
e110209.6%
 
i85657.5%
 
r84977.4%
 
k55214.8%
 
n54534.8%
 
o54314.7%
 
d46564.1%
 
l39773.5%
 
h39773.5%
 
u30662.7%
 
m30662.7%
 
E23652.1%
 
M16121.4%
 
B15441.3%
 
s15441.3%
 
f15441.3%
 
C15221.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
1010962.2%
 
(306618.9%
 
)306618.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII130790100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
c1727213.2%
 
a123549.4%
 
t115638.8%
 
e110208.4%
 
101097.7%
 
i85656.5%
 
r84976.5%
 
k55214.2%
 
n54534.2%
 
o54314.2%
 
d46563.6%
 
l39773.0%
 
h39773.0%
 
(30662.3%
 
u30662.3%
 
m30662.3%
 
)30662.3%
 
E23651.8%
 
M16121.2%
 
B15441.2%
 
s15441.2%
 
f15441.2%
 
C15221.2%
 

Monthly Charges
Real number (ℝ≥0)

Distinct1585
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.76169246
Minimum18.25
Maximum118.75
Zeros0
Zeros (%)0.0%
Memory size55.1 KiB
2021-10-27T13:45:52.946549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum18.25
5-th percentile19.65
Q135.5
median70.35
Q389.85
95-th percentile107.4
Maximum118.75
Range100.5
Interquartile range (IQR)54.35

Descriptive statistics

Standard deviation30.0900471
Coefficient of variation (CV)0.4646272504
Kurtosis-1.257259695
Mean64.76169246
Median Absolute Deviation (MAD)24.05
Skewness-0.2205244339
Sum456116.6
Variance905.4109343
MonotocityNot monotonic
2021-10-27T13:45:53.068958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20.05610.9%
 
19.85450.6%
 
19.95440.6%
 
19.9440.6%
 
20430.6%
 
19.65430.6%
 
19.7430.6%
 
19.55400.6%
 
20.15400.6%
 
20.25390.6%
 
19.75390.6%
 
20.35380.5%
 
19.8380.5%
 
19.6370.5%
 
20.1370.5%
 
20.2350.5%
 
19.5320.5%
 
19.4310.4%
 
20.45310.4%
 
20.4300.4%
 
20.3280.4%
 
20.5280.4%
 
19.45280.4%
 
20.55270.4%
 
19.35250.4%
 
Other values (1560)611786.9%
 
ValueCountFrequency (%) 
18.251< 0.1%
 
18.41< 0.1%
 
18.551< 0.1%
 
18.72< 0.1%
 
18.751< 0.1%
 
18.870.1%
 
18.8550.1%
 
18.92< 0.1%
 
18.9560.1%
 
1970.1%
 
ValueCountFrequency (%) 
118.751< 0.1%
 
118.651< 0.1%
 
118.62< 0.1%
 
118.351< 0.1%
 
118.21< 0.1%
 
117.81< 0.1%
 
117.61< 0.1%
 
117.51< 0.1%
 
117.451< 0.1%
 
117.351< 0.1%
 

Total Charges
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size221.8 KiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
5174 
Yes
1869 
ValueCountFrequency (%) 
No517473.5%
 
Yes186926.5%
 
2021-10-27T13:45:53.146752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
0
5174 
1
1869 
ValueCountFrequency (%) 
0517473.5%
 
1186926.5%
 
2021-10-27T13:45:53.181659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Churn Score
Real number (ℝ≥0)

Distinct85
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.69941786
Minimum5
Maximum100
Zeros0
Zeros (%)0.0%
Memory size55.1 KiB
2021-10-27T13:45:53.258454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile24
Q140
median61
Q375
95-th percentile94
Maximum100
Range95
Interquartile range (IQR)35

Descriptive statistics

Standard deviation21.52513068
Coefficient of variation (CV)0.3667009225
Kurtosis-1.005679127
Mean58.69941786
Median Absolute Deviation (MAD)17
Skewness-0.08983998912
Sum413420
Variance463.3312507
MonotocityNot monotonic
2021-10-27T13:45:53.379274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
801512.1%
 
711482.1%
 
771452.1%
 
671432.0%
 
681412.0%
 
761412.0%
 
691402.0%
 
701402.0%
 
781382.0%
 
721371.9%
 
651361.9%
 
661351.9%
 
751291.8%
 
741281.8%
 
791261.8%
 
731261.8%
 
591041.5%
 
431041.5%
 
461011.4%
 
261011.4%
 
54961.4%
 
49961.4%
 
38961.4%
 
27951.3%
 
33921.3%
 
Other values (60)395456.1%
 
ValueCountFrequency (%) 
51< 0.1%
 
72< 0.1%
 
82< 0.1%
 
93< 0.1%
 
20831.2%
 
21841.2%
 
22821.2%
 
23781.1%
 
24861.2%
 
25851.2%
 
ValueCountFrequency (%) 
100500.7%
 
99540.8%
 
98500.7%
 
97640.9%
 
96520.7%
 
95430.6%
 
94460.7%
 
93470.7%
 
92480.7%
 
91450.6%
 

CLTV
Real number (ℝ≥0)

Distinct3438
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4400.295755
Minimum2003
Maximum6500
Zeros0
Zeros (%)0.0%
Memory size55.1 KiB
2021-10-27T13:45:53.501380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2296
Q13469
median4527
Q35380.5
95-th percentile6087
Maximum6500
Range4497
Interquartile range (IQR)1911.5

Descriptive statistics

Standard deviation1183.057152
Coefficient of variation (CV)0.2688585536
Kurtosis-0.934032483
Mean4400.295755
Median Absolute Deviation (MAD)922
Skewness-0.3116021004
Sum30991283
Variance1399624.225
MonotocityNot monotonic
2021-10-27T13:45:53.613123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
554680.1%
 
474170.1%
 
513770.1%
 
226970.1%
 
411570.1%
 
474570.1%
 
546170.1%
 
436970.1%
 
509270.1%
 
552770.1%
 
591570.1%
 
570760.1%
 
515460.1%
 
582460.1%
 
517260.1%
 
453560.1%
 
522560.1%
 
559760.1%
 
423560.1%
 
455660.1%
 
477860.1%
 
458060.1%
 
575060.1%
 
409960.1%
 
412060.1%
 
Other values (3413)688197.7%
 
ValueCountFrequency (%) 
20033< 0.1%
 
20043< 0.1%
 
20061< 0.1%
 
200740.1%
 
20081< 0.1%
 
20092< 0.1%
 
20103< 0.1%
 
20112< 0.1%
 
20132< 0.1%
 
20141< 0.1%
 
ValueCountFrequency (%) 
65001< 0.1%
 
64992< 0.1%
 
64951< 0.1%
 
64942< 0.1%
 
64923< 0.1%
 
64911< 0.1%
 
64901< 0.1%
 
64891< 0.1%
 
64881< 0.1%
 
64872< 0.1%
 

Churn Reason
Categorical

MISSING

Distinct20
Distinct (%)1.1%
Missing5174
Missing (%)73.5%
Memory size55.1 KiB
Attitude of support person
192 
Competitor offered higher download speeds
189 
Competitor offered more data
162 
Don't know
154 
Competitor made better offer
140 
Other values (15)
1032 
ValueCountFrequency (%) 
Attitude of support person1922.7%
 
Competitor offered higher download speeds1892.7%
 
Competitor offered more data1622.3%
 
Don't know1542.2%
 
Competitor made better offer1402.0%
 
Attitude of service provider1351.9%
 
Competitor had better devices1301.8%
 
Network reliability1031.5%
 
Product dissatisfaction1021.4%
 
Price too high981.4%
 
Service dissatisfaction891.3%
 
Lack of self-service on Website881.2%
 
Extra data charges570.8%
 
Moved530.8%
 
Limited range of services440.6%
 
Long distance charges440.6%
 
Lack of affordable download/upload speed440.6%
 
Poor expertise of phone support200.3%
 
Poor expertise of online support190.3%
 
Deceased60.1%
 
(Missing)517473.5%
 
2021-10-27T13:45:53.732816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-10-27T13:45:53.844752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length41
Median length3
Mean length8.889677694
Min length3

Overview of Unicode Properties

Unique unicode characters37
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n1155018.4%
 
a711611.4%
 
e56589.0%
 
o47517.6%
 
46957.5%
 
t44277.1%
 
r35125.6%
 
i31145.0%
 
d26594.2%
 
s22253.6%
 
f18913.0%
 
p17462.8%
 
c11601.9%
 
m9671.5%
 
h8251.3%
 
u7041.1%
 
v6741.1%
 
l6341.0%
 
C6211.0%
 
b5050.8%
 
w4900.8%
 
g4760.8%
 
k3890.6%
 
A3270.5%
 
P2390.4%
 
Other values (12)12552.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5567288.9%
 
Space Separator46957.5%
 
Uppercase Letter19573.1%
 
Other Punctuation1980.3%
 
Dash Punctuation880.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C62131.7%
 
A32716.7%
 
P23912.2%
 
L22011.2%
 
D1608.2%
 
N1035.3%
 
S894.5%
 
W884.5%
 
E572.9%
 
M532.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n1155020.7%
 
a711612.8%
 
e565810.2%
 
o47518.5%
 
t44278.0%
 
r35126.3%
 
i31145.6%
 
d26594.8%
 
s22254.0%
 
f18913.4%
 
p17463.1%
 
c11602.1%
 
m9671.7%
 
h8251.5%
 
u7041.3%
 
v6741.2%
 
l6341.1%
 
b5050.9%
 
w4900.9%
 
g4760.9%
 
k3890.7%
 
y1030.2%
 
x960.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
4695100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-88100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'15477.8%
 
/4422.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin5762992.0%
 
Common49818.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n1155020.0%
 
a711612.3%
 
e56589.8%
 
o47518.2%
 
t44277.7%
 
r35126.1%
 
i31145.4%
 
d26594.6%
 
s22253.9%
 
f18913.3%
 
p17463.0%
 
c11602.0%
 
m9671.7%
 
h8251.4%
 
u7041.2%
 
v6741.2%
 
l6341.1%
 
C6211.1%
 
b5050.9%
 
w4900.9%
 
g4760.8%
 
k3890.7%
 
A3270.6%
 
P2390.4%
 
L2200.4%
 
Other values (8)7491.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
469594.3%
 
'1543.1%
 
-881.8%
 
/440.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII62610100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n1155018.4%
 
a711611.4%
 
e56589.0%
 
o47517.6%
 
46957.5%
 
t44277.1%
 
r35125.6%
 
i31145.0%
 
d26594.2%
 
s22253.6%
 
f18913.0%
 
p17462.8%
 
c11601.9%
 
m9671.5%
 
h8251.3%
 
u7041.1%
 
v6741.1%
 
l6341.0%
 
C6211.0%
 
b5050.8%
 
w4900.8%
 
g4760.8%
 
k3890.6%
 
A3270.5%
 
P2390.4%
 
Other values (12)12552.0%
 

Interactions

2021-10-27T13:45:41.111476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:41.241552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:41.335561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:41.437289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:41.551934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:41.657692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:41.756430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:41.852761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:41.946771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:42.033787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:42.124552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:42.218296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:42.310041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:42.396920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:42.497650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:42.604400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:42.761988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:42.866663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:42.968170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:43.065271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:43.163998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:43.255710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:43.358485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:43.455219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:43.556948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:43.661625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:43.761840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:43.858235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:43.952841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:44.055525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:44.149273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:44.242973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:44.341852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:44.435501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:44.526216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:44.617458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:44.712204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:44.805967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:44.894759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:44.989581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:45.079970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:45.185144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:45.356725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:45.454138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:45.542901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:45.639993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:45.741735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:45.847407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:45.937487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-27T13:45:53.939550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-27T13:45:54.089600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-27T13:45:54.235609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-27T13:45:54.407784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-10-27T13:45:54.643183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-10-27T13:45:46.229976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:47.141092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-27T13:45:47.400719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Sample

First rows

CustomerIDCountCountryStateCityZip CodeLat LongLatitudeLongitudeGenderSenior CitizenPartnerDependentsTenure MonthsPhone ServiceMultiple LinesInternet ServiceOnline SecurityOnline BackupDevice ProtectionTech SupportStreaming TVStreaming MoviesContractPaperless BillingPayment MethodMonthly ChargesTotal ChargesChurn LabelChurn ValueChurn ScoreCLTVChurn Reason
03668-QPYBK1United StatesCaliforniaLos Angeles9000333.964131, -118.27278333.964131-118.272783MaleNoNoNo2YesNoDSLYesYesNoNoNoNoMonth-to-monthYesMailed check53.85108.15Yes1863239Competitor made better offer
19237-HQITU1United StatesCaliforniaLos Angeles9000534.059281, -118.3074234.059281-118.307420FemaleNoNoYes2YesNoFiber opticNoNoNoNoNoNoMonth-to-monthYesElectronic check70.70151.65Yes1672701Moved
29305-CDSKC1United StatesCaliforniaLos Angeles9000634.048013, -118.29395334.048013-118.293953FemaleNoNoYes8YesYesFiber opticNoNoYesNoYesYesMonth-to-monthYesElectronic check99.65820.5Yes1865372Moved
37892-POOKP1United StatesCaliforniaLos Angeles9001034.062125, -118.31570934.062125-118.315709FemaleNoYesYes28YesYesFiber opticNoNoYesYesYesYesMonth-to-monthYesElectronic check104.803046.05Yes1845003Moved
40280-XJGEX1United StatesCaliforniaLos Angeles9001534.039224, -118.26629334.039224-118.266293MaleNoNoYes49YesYesFiber opticNoYesYesNoYesYesMonth-to-monthYesBank transfer (automatic)103.705036.3Yes1895340Competitor had better devices
54190-MFLUW1United StatesCaliforniaLos Angeles9002034.066367, -118.30986834.066367-118.309868FemaleNoYesNo10YesNoDSLNoNoYesYesNoNoMonth-to-monthNoCredit card (automatic)55.20528.35Yes1785925Competitor offered higher download speeds
68779-QRDMV1United StatesCaliforniaLos Angeles9002234.02381, -118.15658234.023810-118.156582MaleYesNoNo1NoNo phone serviceDSLNoNoYesNoNoYesMonth-to-monthYesElectronic check39.6539.65Yes11005433Competitor offered more data
71066-JKSGK1United StatesCaliforniaLos Angeles9002434.066303, -118.43547934.066303-118.435479MaleNoNoNo1YesNoNoNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceMonth-to-monthNoMailed check20.1520.15Yes1924832Competitor made better offer
86467-CHFZW1United StatesCaliforniaLos Angeles9002834.099869, -118.32684334.099869-118.326843MaleNoYesYes47YesYesFiber opticNoYesNoNoYesYesMonth-to-monthYesElectronic check99.354749.15Yes1775789Competitor had better devices
98665-UTDHZ1United StatesCaliforniaLos Angeles9002934.089953, -118.29482434.089953-118.294824MaleNoYesNo1NoNo phone serviceDSLNoYesNoNoNoNoMonth-to-monthNoElectronic check30.2030.2Yes1972915Competitor had better devices

Last rows

CustomerIDCountCountryStateCityZip CodeLat LongLatitudeLongitudeGenderSenior CitizenPartnerDependentsTenure MonthsPhone ServiceMultiple LinesInternet ServiceOnline SecurityOnline BackupDevice ProtectionTech SupportStreaming TVStreaming MoviesContractPaperless BillingPayment MethodMonthly ChargesTotal ChargesChurn LabelChurn ValueChurn ScoreCLTVChurn Reason
70330871-OPBXW1United StatesCaliforniaTwentynine Palms9227734.17211, -115.76977334.172110-115.769773FemaleNoNoNo2YesNoNoNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceMonth-to-monthYesMailed check20.0539.25No0805191NaN
70343605-JISKB1United StatesCaliforniaTwentynine Palms9227834.457829, -116.13958934.457829-116.139589MaleYesYesNo55YesYesDSLYesYesNoNoNoNoOne yearNoCredit card (automatic)60.003316.1No0714212NaN
70359767-FFLEM1United StatesCaliforniaWestmorland9228133.03679, -115.6050333.036790-115.605030MaleNoNoNo38YesNoFiber opticNoNoNoNoNoNoMonth-to-monthYesCredit card (automatic)69.502625.25No0354591NaN
70368456-QDAVC1United StatesCaliforniaWinterhaven9228332.852947, -114.85078432.852947-114.850784MaleNoNoNo19YesNoFiber opticNoNoNoNoYesNoMonth-to-monthYesBank transfer (automatic)78.701495.1No0202464NaN
70377750-EYXWZ1United StatesCaliforniaYucca Valley9228434.159534, -116.42598434.159534-116.425984FemaleNoNoNo12NoNo phone serviceDSLNoYesYesYesYesYesOne yearNoElectronic check60.65743.3No0243740NaN
70382569-WGERO1United StatesCaliforniaLanders9228534.341737, -116.53941634.341737-116.539416FemaleNoNoNo72YesNoNoNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceTwo yearYesBank transfer (automatic)21.151419.4No0455306NaN
70396840-RESVB1United StatesCaliforniaAdelanto9230134.667815, -117.53618334.667815-117.536183MaleNoYesYes24YesYesDSLYesNoYesYesYesYesOne yearYesMailed check84.801990.5No0592140NaN
70402234-XADUH1United StatesCaliforniaAmboy9230434.559882, -115.63716434.559882-115.637164FemaleNoYesYes72YesYesFiber opticNoYesYesNoYesYesOne yearYesCredit card (automatic)103.207362.9No0715560NaN
70414801-JZAZL1United StatesCaliforniaAngelus Oaks9230534.1678, -116.8643334.167800-116.864330FemaleNoYesYes11NoNo phone serviceDSLYesNoNoNoNoNoMonth-to-monthYesElectronic check29.60346.45No0592793NaN
70423186-AJIEK1United StatesCaliforniaApple Valley9230834.424926, -117.18450334.424926-117.184503MaleNoNoNo66YesNoFiber opticYesNoYesYesYesYesTwo yearYesBank transfer (automatic)105.656844.5No0385097NaN